1,721,006 research outputs found
sj-docx-1-sci-10.1177_00368504221109215 - Supplemental material for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction
No full textSupplemental material, sj-docx-1-sci-10.1177_00368504221109215 for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction by Satanat Kitsiranuwat, Apichat Suratanee and Kitiporn Plaimas in Science Progress</p
sj-xlsx-6-sci-10.1177_00368504221109215 - Supplemental material for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction
No full textSupplemental material, sj-xlsx-6-sci-10.1177_00368504221109215 for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction by Satanat Kitsiranuwat, Apichat Suratanee and Kitiporn Plaimas in Science Progress</p
sj-docx-3-sci-10.1177_00368504221109215 - Supplemental material for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction
No full textSupplemental material, sj-docx-3-sci-10.1177_00368504221109215 for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction by Satanat Kitsiranuwat, Apichat Suratanee and Kitiporn Plaimas in Science Progress</p
sj-xlsx-5-sci-10.1177_00368504221109215 - Supplemental material for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction
No full textSupplemental material, sj-xlsx-5-sci-10.1177_00368504221109215 for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction by Satanat Kitsiranuwat, Apichat Suratanee and Kitiporn Plaimas in Science Progress</p
sj-xlsx-2-sci-10.1177_00368504221109215 - Supplemental material for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction
No full textSupplemental material, sj-xlsx-2-sci-10.1177_00368504221109215 for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction by Satanat Kitsiranuwat, Apichat Suratanee and Kitiporn Plaimas in Science Progress</p
sj-xlsx-4-sci-10.1177_00368504221109215 - Supplemental material for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction
No full textSupplemental material, sj-xlsx-4-sci-10.1177_00368504221109215 for Integration of various protein similarities using random forest technique to infer augmented drug-protein matrix for enhancing drug-disease association prediction by Satanat Kitsiranuwat, Apichat Suratanee and Kitiporn Plaimas in Science Progress</p
Heterogeneous Network Model to Identify Potential Associations Between Plasmodium vivax and Human Proteins
No full textIntegration of multiple sources and data levels provides a great insight into the complex associations between human and malaria systems. In this study, a meta-analysis framework was developed based on a heterogeneous network model for integrating human-malaria protein similarities, a human protein interaction network, and a Plasmodium vivax protein interaction network. An iterative network propagation was performed on the heterogeneous network until we obtained stabilized weights. The association scores were calculated for qualifying a novel potential human-malaria protein association. This method provided a better performance compared to random experiments. After that, the stabilized network was clustered into association modules. The potential association candidates were then thoroughly analyzed by statistical enrichment analysis with protein complexes and known drug targets. The most promising target proteins were the succinate dehydrogenase protein complex in the human citrate (TCA) cycle pathway and the nicotinic acetylcholine receptor in the human central nervous system. Promising associations and potential drug targets were also provided for further studies and designs in therapeutic approaches for malaria at a systematic level. In conclusion, this method is efficient to identify new human-malaria protein associations and can be generalized to infer other types of association studies to further advance biomedical science
Gene Association Classification for Autism Spectrum Disorder: Leveraging Gene Embedding and Differential Gene Expression Profiles to Identify Disease-Related Genes
No full textIdentifying genes associated with autism spectrum disorder (ASD) is crucial for understanding the underlying mechanisms of the disorder. However, ASD is a complex condition involving multiple mechanisms, and this has resulted in an unclear understanding of the disease and a lack of precise knowledge concerning the genes associated with ASD. To address these challenges, we conducted a systematic analysis that integrated multiple data sources, including associations among ASD-associated genes and gene expression data from ASD studies. With these data, we generated both a gene embedding profile that captured the complex relationships between genes and a differential gene expression profile (built from the gene expression data). We utilized the XGBoost classifier and leveraged these profiles to identify novel ASD associations. This approach revealed 10,848 potential gene–gene associations and inferred 125 candidate genes, with DNA Topoisomerase I, ATP Synthase F1 Subunit Gamma, and Neuronal Calcium Sensor 1 being the top three candidates. We conducted a statistical analysis to assess the relevance of candidate genes to specific functions and pathways. Additionally, we identified sub-networks within the candidate network to uncover sub-groups of associations that could facilitate the identification of potential ASD-related genes. Overall, our systematic analysis, which integrated multiple data sources, represents a significant step towards unraveling the complexities of ASD. By combining network-based gene associations, gene expression data, and machine learning, we contribute to ASD research and facilitate the discovery of new targets for molecularly targeted therapies
Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations
No full text The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k-nearest neighbor (R kNN) search. The R kNN search was used to identify an impact of a protein on other proteins. Then, associations between proteins and diseases were inferred statistically. The method using the R kNN search yielded a much higher precision than a random selection, standard nearest neighbor search, or when applying the method to a random protein-protein interaction network. All protein-disease pair candidates were verified by a literature search. Supporting evidence for 596 pairs was identified. In addition, cluster analysis of these candidates revealed 10 promising groups of diseases to be further investigated experimentally. This method can be used to identify novel associations to better understand complex relationships between proteins and diseases. </jats:p
Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions
No full textProtein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several studies have attempted to identify disease-gene associations, the number of possible disease-gene associations is very small. High-throughput technologies have been established experimentally to identify the association between genes and diseases. However, these techniques are still quite expensive, time consuming, and even difficult to perform. Thus, based on currently available data and knowledge, computational methods have served as alternatives to provide more possible associations to increase our understanding of disease mechanisms. Here, a new network-based algorithm, namely, Disease-Gene Association (DGA), was developed to calculate the association score of a query gene to a new possible set of diseases. First, a large-scale protein interaction network was constructed, and the relationship between two interacting proteins was calculated with regard to the disease relationship. Novel plausible disease-gene pairs were identified and statistically scored by our algorithm using neighboring protein information. The results yielded high performance for disease-gene prediction, with an F-measure of 0.78 and an AUC of 0.86. To identify promising candidates of disease-gene associations, the association coverage of genes and diseases were calculated and used with the association score to perform gene and disease selection. Based on gene selection, we identified promising pairs that exhibited evidence related to several important diseases, e.g., inflammation, lipid metabolism, inborn errors, xanthomatosis, cerebellar ataxia, cognitive deterioration, malignant neoplasms of the skin and malignant tumors of the cervix. Focusing on disease selection, we identified target genes that were important to blistering skin diseases and muscular dystrophy. In summary, our developed algorithm is simple, efficiently identifies disease–gene associations in the protein-protein interaction network and provides additional knowledge regarding disease-gene associations. This method can be generalized to other association studies to further advance biomedical science.</div
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